• Journal of the Korean Institute of Landscape Architecture
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• v.52 no.3
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• pp.46-58
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• 2024

The purpose of this study is to understand the current status of the pet plant kit industry and determine the priorities for support policies to revitalize the industry. SWOT analysis assessed the industry's current state, and the Analytic Hierarchy Process (AHP) was used with industry professionals to prioritize support policies. The SWOT analysis results indicated that SO strategies involve leveraging government support policies to enhance marketing and developing eco-friendly DIY products. WO strategies include launching advertising campaigns to increase market recognition and establishing strategic partnerships to expand distribution. ST strategies focus on strengthening price competitiveness and proposing unique values, while WT strategies involve improving production processes and enhancing product quality based on consumer feedback. The AHP analysis identified 3 top-level and 12 sub-level evaluation items, with data collected from 17 expert surveys. The results showed the 'entry phase' (0.482), 'activation phase' (0.397), and 'advanced phase' (0.121) were prioritized, with 'organizing seminars' (0.181) as the most crucial subcategory and 'support for kit development' (0.020) as the least. The pet plant kit industry is in its early stages, and appropriate policy incubation can help activate the garden industry. This study provides foundational information on the industry's needs for activation.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.517-524
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• 2024

This study aims to train and implement a deep learning model for the fusion of website creation and artificial intelligence, in the era known as the AI revolution following the launch of the ChatGPT service. The deep learning model was trained using 3,000 collected web page images, processed based on a system of component and layout classification. This process was divided into three stages. First, prior research on AI models was reviewed to select the most appropriate algorithm for the model we intended to implement. Second, suitable web page and paragraph images were collected, categorized, and processed. Third, the deep learning model was trained, and a serving interface was integrated to verify the actual outcomes of the model. This implemented model will be used to detect multiple paragraphs on a web page, analyzing the number of lines, elements, and features in each paragraph, and deriving meaningful data based on the classification system. This process is expected to evolve, enabling more precise analysis of web pages. Furthermore, it is anticipated that the development of precise analysis techniques will lay the groundwork for research into AI's capability to automatically generate perfect web pages.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.35 no.1
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• pp.47-71
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• 2024

This study aims to compare the awareness of the social value of public libraries between users and non-users to materialize the abstract concept of social value and, thereby, to present measures that can be applied to the library field. For this purpose, a survey was conducted in person or online for 15 days from May 7, 2023, targeting Paju citizens. Frequency analysis, factor analysis, independent samples t-test, and one-way analysis of variance were conducted on 206 valid response copies using SPSS 25.0. In the results, it is shown that, first, for awareness of the social value of public libraries according to socio-demographic characteristics, there were significant differences depending on age, with in particular, awareness among those in their 10s and 40s being higher than other age groups. For awareness of community development, the awareness of the female group was higher than that of the male group, while, for the awareness according to occupation, it is shown that awareness was highest in the following order: student, others, housewife, self-employed, and office worker. Second, for the awareness of the social value of public libraries, the awareness of the user group was higher than that of the non-user group in all areas. Third, for awareness of the social value of public libraries according to the level of library use, there was no significant difference between groups. In conclusion, it is suggested that measures to encourage non-users to become users, develop new content and services targeting male group and those in their 60s, and 20s, and strengthen community activities are needed to raise awareness of the social value of Paju City public libraries in the future.

• Smart Media Journal
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• v.12 no.11
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• pp.27-35
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• 2023

Fourth Industrial Revolution and COVID-19 pandemic have boosted the use of Government 24 app for public service complaints in the era of non-face-to-face interactions. there has been a growing influx of complaints and improvement demands from users of public apps. Furthermore, systematic management of public apps is deemed necessary. The aim of this study is to analyze the grievances of Government 24 app users, understand the current dissatisfaction among citizens, and propose potential improvements. Data were collected from the Google Play Store from May 2, 2013, to June 30, 2023, comprising a total of 6,344 records. Among these, 1,199 records with a rating of 1 and at least one 'thumbs-up' were used for topic modeling analysis. The analysis revealed seven topics: 'Issues with certificate issuance,' 'Website functionality and UI problems,' 'User ID-related issues,' 'Update problems,' 'Government employee app management issues,' 'Budget wastage concerns ((It's not worth even a single star) or (It's a waste of taxpayers' money)),' and 'Password-related problems.' Furthermore, the overall trend of these topics showed an increase until 2021, a slight decrease in 2022, but a resurgence in 2023, underscoring the urgency of updates and management. We hope that the results of this study will contribute to the development and management of public apps that satisfy citizens in the future.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.69-75
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• 2024

This paper proposes an IoT-based indoor autonomous driving system that applies SLAM (Simultaneous Localization And Mapping) and Navigation techniques in a ROS (Robot Operating System) environment based on TurtleBot3. The proposed autonomous driving system can be applied to indoor autonomous wheelchairs and robots. In this study, the operation was verified by applying it to an indoor self-driving wheelchair. The proposed autonomous driving system provides two functions. First, indoor environment information is collected and stored, which allows the wheelchair to recognize obstacles. By performing navigation using the map created through this, the rider can move to the desired location through autonomous driving of the wheelchair. Second, it provides the ability to track and move a specific logo through image recognition using OpenCV. Through this, information services can be received from guides wearing uniforms with the organization's unique logo. The proposed system is expected to provide convenience to passengers by improving mobility, safety, and usability over existing wheelchairs.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.891-899
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• 2024

The Fourth Industrial Revolution is centered on a personalized demand fulfillment economy and is all about transformation and flexible processing that can deliver what customers want in real time across space and time. This paper implements the construction and operation of a packaging platform that can instantly procure the required packaging products based on real-time orders and evaluates its performance. The components of customer satisfaction are flexible and dependent on the situation which requires efficient management of enterprise operational processes based on an e-SCM platform. An OMS optimized for these conditions plays an important role in maximizing and differentiating the efficiency of a company's operations and improving its cost advantage. OMS is a system of mass customization that provides efficient MOT(Moment of Truth) logistics services to meet the eco-friendly issues of many individual customers and achieve optimized logistics operation goals to enhance repurchase intentions and sustainable business. OMS precisely analyzes the collected data to support information and decision-making related to efficiency, productivity, cost and provide accurate reports. It uses data visualization tools to express data visually and suggests directions for improvement of the operational process through statistics and prediction analysis.

• Journal of Internet Computing and Services
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• v.15 no.3
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• pp.45-52
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• 2014

The Ubiquitous-City (U-City) is a smart or intelligent city to satisfy human beings' desire to enjoy IT services with any device, anytime, anywhere. It is a future city model based on Internet of everything or things (IoE or IoT). It includes a lot of video cameras which are networked together. The networked video cameras support a lot of U-City services as one of the main input data together with sensors. They generate huge amount of video information, real big data for the U-City all the time. It is usually required that the U-City manipulates the big data in real-time. And it is not easy at all. Also, many times, it is required that the accumulated video data are analyzed to detect an event or find a figure among them. It requires a lot of computational power and usually takes a lot of time. Currently we can find researches which try to reduce the processing time of the big video data. Cloud computing can be a good solution to address this matter. There are many cloud computing methodologies which can be used to address the matter. MapReduce is an interesting and attractive methodology for it. It has many advantages and is getting popularity in many areas. Video cameras evolve day by day so that the resolution improves sharply. It leads to the exponential growth of the produced data by the networked video cameras. We are coping with real big data when we have to deal with video image data which are produced by the good quality video cameras. A video surveillance system was not useful until we find the cloud computing. But it is now being widely spread in U-Cities since we find some useful methodologies. Video data are unstructured data thus it is not easy to find a good research result of analyzing the data with MapReduce. This paper presents an analyzing system for the video surveillance system, which is a cloud-computing based video data management system. It is easy to deploy, flexible and reliable. It consists of the video manager, the video monitors, the storage for the video images, the storage client and streaming IN component. The "video monitor" for the video images consists of "video translater" and "protocol manager". The "storage" contains MapReduce analyzer. All components were designed according to the functional requirement of video surveillance system. The "streaming IN" component receives the video data from the networked video cameras and delivers them to the "storage client". It also manages the bottleneck of the network to smooth the data stream. The "storage client" receives the video data from the "streaming IN" component and stores them to the storage. It also helps other components to access the storage. The "video monitor" component transfers the video data by smoothly streaming and manages the protocol. The "video translator" sub-component enables users to manage the resolution, the codec and the frame rate of the video image. The "protocol" sub-component manages the Real Time Streaming Protocol (RTSP) and Real Time Messaging Protocol (RTMP). We use Hadoop Distributed File System(HDFS) for the storage of cloud computing. Hadoop stores the data in HDFS and provides the platform that can process data with simple MapReduce programming model. We suggest our own methodology to analyze the video images using MapReduce in this paper. That is, the workflow of video analysis is presented and detailed explanation is given in this paper. The performance evaluation was experiment and we found that our proposed system worked well. The performance evaluation results are presented in this paper with analysis. With our cluster system, we used compressed $1920{\times}1080(FHD)$ resolution video data, H.264 codec and HDFS as video storage. We measured the processing time according to the number of frame per mapper. Tracing the optimal splitting size of input data and the processing time according to the number of node, we found the linearity of the system performance.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.71-89
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• 2016

Sentiment analysis is used for identifying emotions or sentiments embedded in the user generated data such as customer reviews from blogs, social network services, and so on. Various research fields such as computer science and business management can take advantage of this feature to analyze customer-generated opinions. In previous studies, the star rating of a review is regarded as the same as sentiment embedded in the text. However, it does not always correspond to the sentiment polarity. Due to this supposition, previous studies have some limitations in their accuracy. To solve this issue, the present study uses a supervised sentiment classification model to measure a more accurate sentiment polarity. This study aims to propose an advanced sentiment classifier and to discover the correlation between movie reviews and box-office success. The advanced sentiment classifier is based on two supervised machine learning techniques, the Support Vector Machines (SVM) and Feedforward Neural Network (FNN). The sentiment scores of the movie reviews are measured by the sentiment classifier and are analyzed by statistical correlations between movie reviews and box-office success. Movie reviews are collected along with a star-rate. The dataset used in this study consists of 1,258,538 reviews from 175 films gathered from Naver Movie website (movie.naver.com). The results show that the proposed sentiment classifier outperforms Naive Bayes (NB) classifier as its accuracy is about 6% higher than NB. Furthermore, the results indicate that there are positive correlations between the star-rate and the number of audiences, which can be regarded as the box-office success of a movie. The study also shows that there is the mild, positive correlation between the sentiment scores estimated by the classifier and the number of audiences. To verify the applicability of the sentiment scores, an independent sample t-test was conducted. For this, the movies were divided into two groups using the average of sentiment scores. The two groups are significantly different in terms of the star-rated scores.

• Management & Information Systems Review
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• v.36 no.1
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• pp.115-141
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• 2017

This study is to find the key factors of the Internet of Things for development in public sector. In previous studies, it is said that Internet of Things can work digital system without human operation and gives a lot of outputs(information) users. Generally, people are a subject of operating digital system in traditional way, while people are an object on the internet of things. In other words, it is possible to work digital system with only networking from things to things. After all, it is reported that these advantages of the Internet of Things make possible to reduce social costs significantly in public sector. However, despite the strengths of the Internet of Things, there is a specific user acceptance of the technology factor for the Internet of Things rarely. It means that developing of the Internet of Things only focuses on the final purpose. If the focus on development meet this purpose, the user is ignored for the specific reason that using a technique. As a result of this, many users gradually decrease the continuous using of the Internet of Things. Thus, in this study, we need to find what critical factors should reflect to the Internet of Things in public sector. To find this result, there is no choice to use Technology Acceptance Model(TAM). Many researchers have proved that Technology Acceptance Model is valid through the four process in model introduction, confirmation, expansion and refinement from 1986 to 2003. The results of this study showed that the result explanatory power of Internet of Things in public sector is the most important factor affecting only perceived social usefulness and ease of use. Finally, it can be seen that the user has a positive attitude toward use, which has a positive effect on the intention to use continuously. The implications of this study are summarized as follows: When the public Internet of Things service is provided, it means that the user can easily understand the result, and when the person and the object communicate the result to each other, they should be able to communicate with each other. This means that a lot of user effort is needed to understand the outcome of the public Internet of Things being provided.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.127-141
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• 2017

Advances in Internet technologies and the proliferation of mobile devices enabled consumers to approach a wide range of goods and services, while causing an adverse effect that they have hard time reaching their congenial items even if they devote much time to searching for them. Accordingly, businesses are using the recommender systems to provide tools for consumers to find the desired items more easily. Association Rule Mining (ARM) technology is advantageous to recommender systems in that ARM provides intuitive form of a rule with interestingness measures (support, confidence, and lift) describing the relationship between items. Given an item, its relevant items can be distinguished with the help of the measures that show the strength of relationship between items. Based on the strength, the most pertinent items can be chosen among other items and exposed to a given item's web page. However, the diversity of the measures may confuse which items are more recommendable. Given two rules, for example, one rule's support and confidence may not be concurrently superior to the other rule's. Such discrepancy of the measures in distinguishing one rule's superiority from other rules may cause difficulty in selecting proper items for recommendation. In addition, in an online environment where a web page or mobile screen can provide a limited number of recommendations that attract consumer interest, the prudent selection of items to be included in the list of recommendations is very important. The exposure of items of little interest may lead consumers to ignore the recommendations. Then, such consumers will possibly not pay attention to other forms of marketing activities. Therefore, the measures should be aligned with the probability of consumer's acceptance of recommendations. For this reason, this study proposes a model-based approach to combine those measures into one unified measure that can consistently determine the ranking of recommended items. A regression model was designed to describe how well the measures (independent variables; i.e., support, confidence, and lift) explain consumer's acceptance of recommendations (dependent variables, hit rate of recommended items). The model is intuitive to understand and easy to use in that the equation consists of the commonly used measures for ARM and can be used in the estimation of hit rates. The experiment using transaction data from one of the Korea's largest online shopping malls was conducted to show that the proposed model can improve the hit rates of recommendations. From the top of the list to 13th place, recommended items in the higher rakings from the proposed model show the higher hit rates than those from the competitive model's. The result shows that the proposed model's performance is superior to the competitive model's in online recommendation environment. In a web page, consumers are provided around ten recommendations with which the proposed model outperforms. Moreover, a mobile device cannot expose many items simultaneously due to its limited screen size. Therefore, the result shows that the newly devised recommendation technique is suitable for the mobile recommender systems. While this study has been conducted to cover the cross-selling in online shopping malls that handle merchandise, the proposed method can be expected to be applied in various situations under which association rules apply. For example, this model can be applied to medical diagnostic systems that predict candidate diseases from a patient's symptoms. To increase the efficiency of the model, additional variables will need to be considered for the elaboration of the model in future studies. For example, price can be a good candidate for an explanatory variable because it has a major impact on consumer purchase decisions. If the prices of recommended items are much higher than the items in which a consumer is interested, the consumer may hesitate to accept the recommendations.